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Abstract

A new method is presented that identifies basic color terms (BCTs) from
color-naming data. A function is defined that measures how well a term
is understood by a communicating population. BCTs are then separated
from other color terms by a threshold value applied to this function.
A new mathematical algorithm is proposed and analyzed for determining
the best exemplar associated with each BCT. Using data provided by the
World Color Survey, comparisons are made between the paper’s
methods and those from other studies. These comparisons show that the
paper’s new definition of “basicness” mostly
agrees with the typical definition found in the color categorization
literature, which was originally due to Kay and colleagues. The new
definition, unlike the typical one, has the advantage of not relying
on syntactic or semantic features of languages or color lexicons. This
permits the methodology developed to be generalizable and applied to
other category domains for which a construct of
“basicness” could have an important role.

References

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Table 1.

a The number of WCS color terms classified as BCTs,
“potentially basic” color terms (PBCTs), or
nonbasic color terms (NBCTs) according to the compared
B&K- and CS-definitions.

Table 2.

Brief Comparison of B&K-BCTs and CS-BCTs on
Eight Languages (Emphasized Previously in [36,42]) from the
WCSa

L

B&K

CS

Result

16: Buglere (Panama/Costa Rica)

6

6

20: Candoshi (Peru)

7

7

(+1)(−1)

51: Kalam (Peru)

6

6

56: Konkomba (Ghana/Togo)

4

4

60: Kwerba (Indonesia)

4?

4

(+1?)

64: Martu Wangka (Brazil)

5?

5

(+1?)

74: Mura Piraha (Australia)

4

4

87: Siriono (Bolivia)

5

5

a For each language, the number of B&K-BCTs is
listed, as well as the number of CS-BCTs. A mark
of (+n) indicates that
our method identified n terms that are
not B&K-basic; a mark of
(−m) indicates that
our method did not identify
m B&K-basic
terms as CS-basic. A question mark is used to
indicate B&K color terms that were
classified as “potentially
BCTs.”

Table 3.

Detailed Comparison of B&K-BCTs and CS-BCTs on
Eight Languages from the WCSa

Language 16: Buglere

c

B&K

CS

Q16(c)

1. jutre

✓

✓

0.623

2. jere

✓

✓

0.537

3. dabe

✓

✓

0.528

4. moloin

✓

✓

0.532

5. lere

✓

✓

0.644

6. leren

✓

✓

0.383

Language 20: Candoshi

c

B&K

CS

Q20(c)

1. borshi

✓

✓

0.764

2. chobiapi

✓

✓

0.725

4. kamachpa

✓

✓

0.404

5. kantsiripi

✓

✓

0.730

6. kavabana

✓

✓

0.723

12. pozani

×

✓

0.200

13. ptsiyaro

✓

✓

0.730

Language 51: Kalam

c

B&K

CS

Q51(c)

1. muk

✓

✓

0.472

2. minj-kimemb

✓

✓

0.448

3. likan

✓

✓

0.584

4. tund

✓

✓

0.732

5. mosimb

✓

✓

0.177

9. walin

✓

✓

0.579

Language 56: Konkomba

c

B&K

CS

Q56(c)

1. pipin

✓

✓

0.712

2. bombon

✓

✓

0.638

3. maman

✓

✓

0.732

4. yaankal

✓

✓

0.245

Language 60: Kwerba

c

B&K

CS

Q60(c)

1. asiram

✓

✓

0.623

6. icem

✓

✓

0.618

11. kainanesenum

?

✓

0.182

17. nokonum

✓

✓

0.785

Language 64: Martu Wangka

c

B&K

CS

Q64(c)

10. karntawarra

?

✓

0.189

25. maru-maru

✓

✓

0.633

26. miji-miji

✓

✓

0.621

38. piila-piila

✓

✓

0.317

48. yukuri-yukuri

✓

✓

0.574

Language 74: Mura Piraha

c

B&K

CS

Q74(c)

1. bii sai

✓

✓

0.829

2. biopaiai

✓

✓

0.656

3. ahoasaaga

✓

✓

0.723

4. kobiai

✓

✓

0.768

Language 87: Siriono

c

B&K

CS

Q16(c)

2. echo

✓

✓

0.521

4. eirei

✓

✓

0.748

7. erondei

✓

✓

0.530

8. eruba

✓

✓

0.495

9. eshi

✓

✓

0.721

a For each language, the corresponding table shows
which color terms are B&K-basic and which
color terms are CS-basic; checkmarks denote
basicness, crosses denote nonbasicness, and
question marks denote ambiguity
(“potentially basic” terms). The
strengths of each color term are also provided,
rounded to the nearest thousandth. Color terms
which do not satisfy either definition and also
have strength less than 0.16 have been omitted for
brevity. The numbering assigned by the WCS Data
Archives is used for the languages and color
terms.

Table 4.

“CS-Basic” and CS-“Potentially
Basic” Color Terms That Do Not Have WCS
Focus-Choice Dataa

Language

Color Term

Strength of Color Term

B&K-Basic

3

15. pensaal

0.8161

yes

53

1. ikura

0.1993

no

53

2. iura

0.7741

yes

53

3. ilyby

0.8565

no

66

4. canga/cangu

0.7446

yes

78

7. istak

0.7799

yes

80

3. koomagi

0.2325

yes

80

10. wigium

0.2636

yes

97

5. matak

0.1993

yes

a Languages and color terms are numbered according to the
WCS Data Archive.

a Indicates uncommon events (0.75% of the cases) where
some computed data yielded centers of modal maps that
were not observed to correspond to the categorical
color terms typically used to name color space
regions.

Tables (5)

Table 1.

a The number of WCS color terms classified as BCTs,
“potentially basic” color terms (PBCTs), or
nonbasic color terms (NBCTs) according to the compared
B&K- and CS-definitions.

Table 2.

Brief Comparison of B&K-BCTs and CS-BCTs on
Eight Languages (Emphasized Previously in [36,42]) from the
WCSa

L

B&K

CS

Result

16: Buglere (Panama/Costa Rica)

6

6

20: Candoshi (Peru)

7

7

(+1)(−1)

51: Kalam (Peru)

6

6

56: Konkomba (Ghana/Togo)

4

4

60: Kwerba (Indonesia)

4?

4

(+1?)

64: Martu Wangka (Brazil)

5?

5

(+1?)

74: Mura Piraha (Australia)

4

4

87: Siriono (Bolivia)

5

5

a For each language, the number of B&K-BCTs is
listed, as well as the number of CS-BCTs. A mark
of (+n) indicates that
our method identified n terms that are
not B&K-basic; a mark of
(−m) indicates that
our method did not identify
m B&K-basic
terms as CS-basic. A question mark is used to
indicate B&K color terms that were
classified as “potentially
BCTs.”

Table 3.

Detailed Comparison of B&K-BCTs and CS-BCTs on
Eight Languages from the WCSa

Language 16: Buglere

c

B&K

CS

Q16(c)

1. jutre

✓

✓

0.623

2. jere

✓

✓

0.537

3. dabe

✓

✓

0.528

4. moloin

✓

✓

0.532

5. lere

✓

✓

0.644

6. leren

✓

✓

0.383

Language 20: Candoshi

c

B&K

CS

Q20(c)

1. borshi

✓

✓

0.764

2. chobiapi

✓

✓

0.725

4. kamachpa

✓

✓

0.404

5. kantsiripi

✓

✓

0.730

6. kavabana

✓

✓

0.723

12. pozani

×

✓

0.200

13. ptsiyaro

✓

✓

0.730

Language 51: Kalam

c

B&K

CS

Q51(c)

1. muk

✓

✓

0.472

2. minj-kimemb

✓

✓

0.448

3. likan

✓

✓

0.584

4. tund

✓

✓

0.732

5. mosimb

✓

✓

0.177

9. walin

✓

✓

0.579

Language 56: Konkomba

c

B&K

CS

Q56(c)

1. pipin

✓

✓

0.712

2. bombon

✓

✓

0.638

3. maman

✓

✓

0.732

4. yaankal

✓

✓

0.245

Language 60: Kwerba

c

B&K

CS

Q60(c)

1. asiram

✓

✓

0.623

6. icem

✓

✓

0.618

11. kainanesenum

?

✓

0.182

17. nokonum

✓

✓

0.785

Language 64: Martu Wangka

c

B&K

CS

Q64(c)

10. karntawarra

?

✓

0.189

25. maru-maru

✓

✓

0.633

26. miji-miji

✓

✓

0.621

38. piila-piila

✓

✓

0.317

48. yukuri-yukuri

✓

✓

0.574

Language 74: Mura Piraha

c

B&K

CS

Q74(c)

1. bii sai

✓

✓

0.829

2. biopaiai

✓

✓

0.656

3. ahoasaaga

✓

✓

0.723

4. kobiai

✓

✓

0.768

Language 87: Siriono

c

B&K

CS

Q16(c)

2. echo

✓

✓

0.521

4. eirei

✓

✓

0.748

7. erondei

✓

✓

0.530

8. eruba

✓

✓

0.495

9. eshi

✓

✓

0.721

a For each language, the corresponding table shows
which color terms are B&K-basic and which
color terms are CS-basic; checkmarks denote
basicness, crosses denote nonbasicness, and
question marks denote ambiguity
(“potentially basic” terms). The
strengths of each color term are also provided,
rounded to the nearest thousandth. Color terms
which do not satisfy either definition and also
have strength less than 0.16 have been omitted for
brevity. The numbering assigned by the WCS Data
Archives is used for the languages and color
terms.

Table 4.

“CS-Basic” and CS-“Potentially
Basic” Color Terms That Do Not Have WCS
Focus-Choice Dataa

Language

Color Term

Strength of Color Term

B&K-Basic

3

15. pensaal

0.8161

yes

53

1. ikura

0.1993

no

53

2. iura

0.7741

yes

53

3. ilyby

0.8565

no

66

4. canga/cangu

0.7446

yes

78

7. istak

0.7799

yes

80

3. koomagi

0.2325

yes

80

10. wigium

0.2636

yes

97

5. matak

0.1993

yes

a Languages and color terms are numbered according to the
WCS Data Archive.

a Indicates uncommon events (0.75% of the cases) where
some computed data yielded centers of modal maps that
were not observed to correspond to the categorical
color terms typically used to name color space
regions.